{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6875ece4-37ee-43fb-a146-d5239c6d94f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc3578f1-0869-423f-979c-e2efcfa6d301",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import patches\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import sklearn\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ab92b4-ddf6-44d6-91ff-74bf1a467c36",
   "metadata": {},
   "source": [
    "方差膨胀因子（VIF）是衡量多元线性回归模型中多重共线性的一种度量。VIF的计算公式为:\n",
    "\n",
    "$VIF= \\frac{1}{1-R^2_i}$\n",
    "\n",
    "其中，$R_i$是自变量$X_i$对其余自变量作回归分析的负相关系数。VIF越大，自变量间存在多重共线性的可能性就越高。一般来说，如果VIF大于5，解释变量$X_i$就于其他变量存在高度的多重共线性，参数估计将出现较大的标准差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ad18a99-5140-4e32-8cc9-909953725fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that converts categorical values into numerical values via ordinal encoding or one-hot encoding\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# Function to split data into different groups\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "# Statistics functions\n",
    "from scipy.stats import norm\n",
    "from scipy import stats\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b69a035-46cd-4a52-be5e-9ba8022f3490",
   "metadata": {},
   "outputs": [],
   "source": [
    "#忽略警告\n",
    "warnings.filterwarnings(\"ignore\") \n",
    "\n",
    "#一个在 jupyter Notebook中展示图像的魔法函数\n",
    "%matplotlib inline \n",
    "\n",
    "#改变seaborn中默认的坐标轴大小\n",
    "sns.set(rc={\"figure.figsize\": (20, 15)})\n",
    "\n",
    "#设置绘图默认的背景模式\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5506050c-387b-48d5-9e75-d080469c50d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 81), (1459, 80))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "path = os.path.join('.', 'train.csv')\n",
    "\n",
    "df_train = pd.read_csv(path)\n",
    "df_test = pd.read_csv(os.path.join('.', 'test.csv'))\n",
    "df_train.shape,df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57d0f9eb-040e-4c49-a9c0-b1065b92775b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1b61cb5-b0f0-47be-8b50-3ce1786ffc22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     588 non-null    object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "011aeca4-ccdc-4afa-a5b5-7fe10e14cd55",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.drop(['Id'], axis=1, inplace=True)\n",
    "Id_test_list = df_test[\"Id\"].tolist()\n",
    "df_test.drop(['Id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72a16482-d945-47fc-a193-79d50959de25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  WoodDeckSF  \\\n",
       "0          2003       196.0         706           0  ...           0   \n",
       "1          1976         0.0         978           0  ...         298   \n",
       "2          2002       162.0         486           0  ...           0   \n",
       "3          1970         0.0         216           0  ...           0   \n",
       "4          2000       350.0         655           0  ...         192   \n",
       "\n",
       "   OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  \\\n",
       "0           61              0          0            0         0        0   \n",
       "1            0              0          0            0         0        0   \n",
       "2           42              0          0            0         0        0   \n",
       "3           35            272          0            0         0        0   \n",
       "4           84              0          0            0         0        0   \n",
       "\n",
       "   MoSold  YrSold  SalePrice  \n",
       "0       2    2008     208500  \n",
       "1       5    2007     181500  \n",
       "2       9    2008     223500  \n",
       "3       2    2006     140000  \n",
       "4      12    2008     250000  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#提取数值特征\n",
    "df_train_num = df_train.select_dtypes(include=[np.number])\n",
    "df_train_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a72d74e-9882-4be3-88b7-ffd005124f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>...</td>\n",
       "      <td>730.0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>108.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>312.0</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>482.0</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>20.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>470.0</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>506.0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          20         80.0    11622            5            6       1961   \n",
       "1          20         81.0    14267            6            6       1958   \n",
       "2          60         74.0    13830            5            5       1997   \n",
       "3          60         78.0     9978            6            6       1998   \n",
       "4         120         43.0     5005            8            5       1992   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  GarageArea  \\\n",
       "0          1961         0.0       468.0       144.0  ...       730.0   \n",
       "1          1958       108.0       923.0         0.0  ...       312.0   \n",
       "2          1998         0.0       791.0         0.0  ...       482.0   \n",
       "3          1998        20.0       602.0         0.0  ...       470.0   \n",
       "4          1992         0.0       263.0         0.0  ...       506.0   \n",
       "\n",
       "   WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
       "0         140            0              0          0          120         0   \n",
       "1         393           36              0          0            0         0   \n",
       "2         212           34              0          0            0         0   \n",
       "3         360           36              0          0            0         0   \n",
       "4           0           82              0          0          144         0   \n",
       "\n",
       "   MiscVal  MoSold  YrSold  \n",
       "0        0       6    2010  \n",
       "1    12500       6    2010  \n",
       "2        0       3    2010  \n",
       "3        0       6    2010  \n",
       "4        0       1    2010  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_num = df_test.select_dtypes(include=[np.number])\n",
    "df_test_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "37d6acbc-c182-491b-9965-4a2c0a38519d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x3000 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画出训练集中每个数值变量的分布直方图\n",
    "fig_ = df_train_num.hist(figsize=(25, 30), bins=20, color=\"darkcyan\", edgecolor=\"black\", xlabelsize=8, ylabelsize=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7023aed8-0054-4bcf-b5c5-bc71294b6777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x3000 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_ = df_train_num.hist(figsize=(25, 30), bins=50, color=\"darkcyan\", edgecolor=\"black\", xlabelsize=8, ylabelsize=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb01c31c-9c6f-49d0-bf7e-a7c45ca27cd3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
